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K-nearest neighbor (k-NN) classifier can learn non-linear decision surface and requires only one hyperparameter (i.e. value of “k”) for training. The classification accuracy improves as we increase the amount of training data. With an increase in the amount of training data, computational and memory requirements also increases as it has to store and search through the entire training data for classification...
This paper proposes a novel speaker adaptation algorithm for classifying speech based on deep neural networks (DNNs). The adaptation algorithm consists of two steps. In the first step a deep neural network is trained using raw Mel-frequency cepstral coefficient (MFCC) features to discover hidden structures in the data and employing the activations of the last hidden layers of the DNN as acoustic features...
Speech recognition systems are either based on parametric approach or non-parametric approach. Parametric based systems such as HMMs have been the dominant technology for speech recognition in the past decade. Despite a lot of advancements and enhancements in the design of these systems: key problems such as long term temporal dependence, etc. Has not yet been solved. Recently due to availability...
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